Utilization of Big Data in Educational Technology Research
DOI:
https://doi.org/10.35870/ijecs.v4i1.2643Keywords:
Big Data, Educational Technology, Online Learning, Learning Analytics, Student PerformanceAbstract
This study critically examines the implementation of big data analytics within the field of Educational Technology, with a specific focus on its application at the Faculty of Engineering, Langlangbuana University. The research is prompted by the unprecedented shift from traditional, in-person instruction to online learning environments, a transition significantly expedited by the COVID-19 pandemic. This paper explores how big data can be systematically utilized to develop advanced educational strategies that address the complexities of modern learning environments. Through an extensive review of relevant local and international literature, the study highlights the potential of big data to offer granular insights into student engagement, learning outcomes, and instructional effectiveness. The findings suggest that the strategic integration of big data in educational research not only facilitates personalized learning and improves pedagogical practices but also enhances institutional decision-making processes. This research underscores the critical role of big data in fostering an adaptive, evidence-based approach to contemporary educational challenges.
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References
Agustini, K. (2017). The adaptive eLearning system design: Student learning style trend analysis. In Proceedings of 2nd International Conference on Innovative Research Across Disciplines (ICIRAD) (pp. 50-54). https://doi.org/10.2991/icirad-17.2017.12.
Akrami, K. (2024). Investigating the integration of big data technologies in higher education settings. IJMST, 2(2), 1-12. https://doi.org/10.31004/ijmst.v2i2.296
Al-Rahmi, W., Yahaya, N., Aldraiweesh, A., Alturki, U., Alamri, M., Saud, M., & Alhamed, O. (2019). Big data adoption and knowledge management sharing: an empirical investigation on their adoption and sustainability as a purpose of education. IEEE Access, 7, 47245-47258. https://doi.org/10.1109/access.2019.2906668
Bai, H. (2024). Design and application of decision support system for educational management based on big data. JES, 20(6s), 1645-1655. https://doi.org/10.52783/jes.3084
Cusumano, M. (2013). MOOCs: Contexts and consequences. Communications of the ACM, 56(4), 31-33. https://doi.org/10.1145/2436256.2436266
Daniel, B. (2014). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904-920. https://doi.org/10.1111/bjet.12230
Di, X., Zhu, D., & Wen-hai, X. (2021). The teaching pattern of law majors using artificial intelligence and deep neural network under educational psychology. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.711520
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